Neural network and multiple regression models for PM10 prediction in Athens: A comparative assessment

Chaloulakou, A; Grivas, G; Spyrellis, N

dc.contributor.author	Chaloulakou, A	en
dc.contributor.author	Grivas, G	en
dc.contributor.author	Spyrellis, N	en
dc.date.accessioned	2014-03-01T01:19:19Z
dc.date.available	2014-03-01T01:19:19Z
dc.date.issued	2003	en
dc.identifier.issn	1047-3289	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/15417
dc.relation.uri	http://www.scopus.com/inward/record.url?eid=2-s2.0-0141483644&partnerID=40&md5=bac6182cd3c5da5146a134101e4923d4	en
dc.subject.classification	Engineering, Environmental	en
dc.subject.classification	Environmental Sciences	en
dc.subject.classification	Meteorology & Atmospheric Sciences	en
dc.subject.other	Air quality	en
dc.subject.other	Health	en
dc.subject.other	Neural networks	en
dc.subject.other	Particles (particulate matter)	en
dc.subject.other	Regression analysis	en
dc.subject.other	Public awareness	en
dc.subject.other	Air pollution control	en
dc.subject.other	air pollution	en
dc.subject.other	airborne particle	en
dc.subject.other	analytical error	en
dc.subject.other	article	en
dc.subject.other	artificial neural network	en
dc.subject.other	Greece	en
dc.subject.other	linear regression analysis	en
dc.subject.other	multiple regression	en
dc.subject.other	particulate matter	en
dc.subject.other	priority journal	en
dc.subject.other	statistical model	en
dc.subject.other	Air Pollutants	en
dc.subject.other	Cities	en
dc.subject.other	Forecasting	en
dc.subject.other	Greece	en
dc.subject.other	Neural Networks (Computer)	en
dc.subject.other	Particle Size	en
dc.subject.other	Regression Analysis	en
dc.title	Neural network and multiple regression models for PM10 prediction in Athens: A comparative assessment	en
heal.type	journalArticle	en
heal.language	English	en
heal.publicationDate	2003	en
heal.abstract	Particulate atmospheric pollution in urban areas is considered to have significant impact on human health. Therefore, the ability to make accurate predictions of particulate ambient concentrations is important to improve public awareness and air quality management. This study examines the possibility of using neural network methods as tools for daily average particulate matter with aerodynamic diameter <10 μm (PM10) concentration forecasting, providing an alternative to statistical models widely used up to this day. Based on a data inventory, in a fixed central site in Athens, Greece, ranging over a two-year period, and using mainly meteorological variables as inputs, neural network models and multiple linear regression models were developed and evaluated. Comparison statistics used indicate that the neural network approach has an edge over regression models, expressed both in terms of prediction error (root mean square error values lower by 8.2-9.4%) and of episodic prediction ability (false alarm rate values lower by 7-13%). The results demonstrate that artificial neural networks (ANNs), if properly trained and formed, can provide adequate solutions to particulate pollution prognostic demands.	en
heal.publisher	AIR & WASTE MANAGEMENT ASSOC	en
heal.journalName	Journal of the Air and Waste Management Association	en
dc.identifier.isi	ISI:000185765500003	en
dc.identifier.volume	53	en
dc.identifier.issue	10	en
dc.identifier.spage	1183	en
dc.identifier.epage	1190	en